File size: 4,550 Bytes
c2f35d3 2a47c9d c13a51c 27cb976 c2f35d3 27cb976 c2f35d3 2a47c9d c2f35d3 eecd0bd c2f35d3 eecd0bd c2f35d3 eecd0bd c2f35d3 818ca9e c2f35d3 818ca9e c13a51c 818ca9e c13a51c 818ca9e d74685a 818ca9e c2f35d3 1510fcd c2f35d3 5a6be21 c13a51c 818ca9e d74685a c2f35d3 818ca9e c2f35d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import gradio as gr
import os
hf_token = os.environ.get("HF_TOKEN")
import spaces
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import torch
import time
class Dummy():
pass
resolutions = ["1024 1024","1280 768","1344 768","768 1344","768 1280"]
# Ng
default_negative_prompt= "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
# Load pipeline
model_id = "briaai/BRIA-2.2"
scheduler = EulerAncestralDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
steps_offset=1
)
pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
pipe.force_zeros_for_empty_prompt = False
# print("Optimizing BRIA-2.2 - this could take a while")
# t=time.time()
# pipe.unet = torch.compile(
# pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
# )
# with torch.no_grad():
# outputs = pipe(
# prompt="an apple",
# num_inference_steps=30,
# )
# # This will avoid future compilations on different shapes
# unet_compiled = torch._dynamo.run(pipe.unet)
# unet_compiled.config=pipe.unet.config
# unet_compiled.add_embedding = Dummy()
# unet_compiled.add_embedding.linear_1 = Dummy()
# unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
# pipe.unet = unet_compiled
# print(f"Optimizing finished successfully after {time.time()-t} secs")
@spaces.GPU(enable_queue=True)
def infer(prompt,negative_prompt,seed,resolution):
print(f"""
—/n
{prompt}
""")
# generator = torch.Generator("cuda").manual_seed(555)
t=time.time()
if seed=="-1":
generator=None
else:
try:
seed=int(seed)
generator = torch.Generator("cuda").manual_seed(seed)
except:
generator=None
w,h = resolution.split()
w,h = int(w),int(h)
image = pipe(prompt,num_inference_steps=30, negative_prompt=negative_prompt,generator=generator,width=w,height=h).images[0]
print(f'gen time is {time.time()-t} secs')
# Future
# Add amound of steps
# if nsfw:
# raise gr.Error("Generated image is NSFW")
return image
css = """
#col-container{
margin: 0 auto;
max-width: 580px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("## BRIA 2.2 Beta")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is a demo for
<a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image </a>.
BRIA 2.2 improve the realism of BRIA 2.0 while still trained on licensed data, and so provide full legal liability coverage for copyright and privacy infringement.
</p>
''')
with gr.Group():
with gr.Column():
prompt_in = gr.Textbox(label="Prompt", value="A smiling man with wavy brown hair and a trimmed beard")
resolution = gr.Dropdown(value=resolutions[0], show_label=True, label="Resolution", choices=resolutions)
seed = gr.Textbox(label="Seed", value=-1)
negative_prompt = gr.Textbox(label="Negative Prompt", value=default_negative_prompt)
submit_btn = gr.Button("Generate")
result = gr.Image(label="BRIA-2.2 Result")
# gr.Examples(
# examples = [
# "Dragon, digital art, by Greg Rutkowski",
# "Armored knight holding sword",
# "A flat roof villa near a river with black walls and huge windows",
# "A calm and peaceful office",
# "Pirate guinea pig"
# ],
# fn = infer,
# inputs = [
# prompt_in
# ],
# outputs = [
# result
# ]
# )
submit_btn.click(
fn = infer,
inputs = [
prompt_in,
negative_prompt,
seed,
resolution
],
outputs = [
result
]
)
demo.queue().launch(show_api=False) |